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Databricks vs PyTorch

Databricks logo

Databricks

Software

Unified analytics platform for data engineering and data science

From
Free
Rated
-
Full reviewWebsite
PyTorch logo

PyTorch

Software

Deep learning framework with dynamic computation graphs

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges; PyTorch dynamic computation graph can be less efficient for production inference than static graphs
  • They diverge on capability: Databricks covers Delta Lake, PyTorch covers Dynamic computation graphs.

Where they differ

Only the attributes on which Databricks and PyTorch actually diverge.

Attributes where Databricks and PyTorch differ
AttributeDatabricksPyTorch
Pricing modelusage-basedUnknown
PlatformsWeb, Aws, Azure, GcpLinux, Windows, macOS
Founded20132016

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Databricks

  • Delta Lake
  • Apache Spark
  • MLflow
  • Unity Catalog
  • Photon Engine
  • Collaborative Notebooks
  • Auto-scaling
  • AWS

Only in PyTorch

  • Dynamic computation graphs
  • Automatic differentiation
  • GPU acceleration
  • Distributed training
  • TorchScript
  • TorchVision
  • TorchText
  • TorchAudio

What people use each for

The jobs each tool is most often brought in to do.

Databricks

  • Running Spark data engineering pipelines on managed clustersnot PyTorch
  • Building a lakehouse over data in cloud object storagenot PyTorch
  • Training and serving machine learning models alongside the datanot PyTorch

PyTorch

  • Machine learningnot Databricks
  • Data analysisnot Databricks
  • Model trainingnot Databricks
  • Predictive analyticsnot Databricks

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Databricks

  • Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
  • The free trial lasts 14 days
  • Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
  • Azure Databricks pricing is set by Microsoft rather than by Databricks
  • Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate

PyTorch

  • Dynamic computation graph can be less efficient for production inference than static graphs
  • Requires more manual code for distributed training compared to some alternatives
  • Documentation focused heavily on research use cases rather than production deployment

Pricing, plan by plan

Databricks

Free
  • Community EditionFree
    • Limited cluster
    • Notebook environment
    • Community support
  • Standard$0.07/DBU
    • Jobs compute
    • SQL compute
    • Standard support

PyTorch

Free

No published plan breakdown. See the PyTorch review.

Which should you pick?

Choose Databricks if

  • You need delta lake.
  • You want to start without paying.
  • You work on Web, Aws, Azure, Gcp.
  • You also want apache spark.
Databricks review

Choose PyTorch if

  • You need dynamic computation graphs.
  • You want to start without paying.
  • You work on Linux, Windows, macOS.
  • You also want automatic differentiation.
PyTorch review

Questions people ask

Is Databricks or PyTorch better?
Neither clearly leads. Databricks starts at Free and PyTorch at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Databricks or PyTorch?
Databricks starts at Free and PyTorch at Free.
Does Databricks or PyTorch run on more platforms?
Databricks runs on Web, Aws, Azure, Gcp. PyTorch runs on Linux, Windows, macOS.
Can I use Databricks for free?
Both have a free tier, so you can try either at no cost before committing.
What is Databricks best used for?
Databricks is most often used for running spark data engineering pipelines on managed clusters, building a lakehouse over data in cloud object storage, training and serving machine learning models alongside the data. Of those, running spark data engineering pipelines on managed clusters and building a lakehouse over data in cloud object storage are not what PyTorch is typically brought in for.
What can Databricks do that PyTorch cannot?
Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog. PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training.

Answered from the vendors’ own pages

PyTorch: Is PyTorch free and open source?

Yes. PyTorch is an open source machine learning framework that is completely free to use. It was originally created and open-sourced by Facebook (now Meta) in 2016.

Source
PyTorch: What platforms does PyTorch support?

PyTorch supports Linux, Windows, and macOS. It provides strong GPU acceleration through CUDA and other backends for high-performance computing.

Source
PyTorch: Can I use PyTorch for production deployments?

Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.

Source
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